Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Detection and Discrimination of Injected Network Faults

Abstract Although the present work does in fact employ training data, it does so in the interest of calibrating the results Six hundred faults were induced by injection into five live obtained from an experimental detection and diagnostic campus networks at Carnegie Mellon University in order system designed specifically to accommodate noisy, to determine whether or not particular network faults nonstationary, nonspecific domains. The system have unique signatures as determined by out-of-band generalizes by virtue of its log analysis capabilities; all monitoring instrumentation. If unique signatures span monitored data and events are recorded in log files. networks, then the monitoring instrumentation can be These files are processed by the system, resulting in used to diagnose network faults, or distinguish among testable and reproducible detections and diagnoses of fault classes, without human intervention, using anomalous conditions. Any monitored process or machine-generated diagnostic decision rules. This device can be used to populate the logs with data. would be especially useful in large, unmanned systems in which the occurrence of novel or unanticipated faults The specific objective of the present work is to conduct could be catastrophic. Results indicate that significant a designed experiment to test the detection and diagaccuracy in automated detection and discrimination nosis capabilities of a system for handling faults in local among fault types can be obtained using anomaly sigarea networks. Networks were selected as a test natures as described here. domain because their operating characteristics include nonlinear, nonstationary dynamic behavior. The experiment uses automated injection techniques to induc

Design and evaluation of crash tolerant protocols for mobile ad-hoc networks

Mobile ad-hoc networks are wireless networks operating without any form of supporting infrastructure such as base-stations, and thus require the participating nodes to co-operate by forwarding each other's messages. Ad-hoc networks can be deployed when installing network infrastructure is considered too expensive, too cumbersome or simply too slow, for example in domains such as battlefields, search-and-rescue or space exploration. Tolerating node crashes and transient network partitions is likely to be important in such domains. However, developing applications which do so is a difficult task, a task which can be made easier by the availability of fault-tolerant protocols and middleware. This dissertation studies two core fault-tolerant primitives, reliable dissemination and consensus, and presents two families of protocols which implement these primitives in a wide range of mobile ad-hoc networks. The performance of the protocols is studied through simulation indicating that they are able to provide their guarantees in a bandwidth efficient manner. This is achieved by taking advantage of the broadcast nature and variable message delivery latencies inherent in ad-hoc networks. To illustrate the usefulness of these two primitives, a design for a distributed, fault-tolerant tuple space suitable to implement on mobile ad-hoc networks is presented. This design, if implemented, would provide a simple, yet powerful abstraction to the developer of fault-tolerant applications in mobile ad-hoc networks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Majority Lemma for Randomised Query Complexity

We show that computing the majority of n copies of a boolean function g has randomised query complexity R(Maj?g?) = ?(n?R ?_{1/n}(g)). In fact, we show that to obtain a similar result for any composed function f?g?, it suffices to prove a sufficiently strong form of the result only in the special case g = GapOr

A Combined Analytical Modeling Machine Learning Approach for Performance Prediction of MapReduce Jobs in Hadoop Clusters

Nowadays MapReduce and its open source implementation, Apache Hadoop, are the most widespread solutions for handling massive dataset on clusters of commodity hardware. At the expense of a somewhat reduced performance in comparison to HPC technologies, the MapReduce framework provides fault tolerance and automatic parallelization without any efforts by developers. Since in many cases Hadoop is adopted to support business critical activities, it is often important to predict with fair confidence the execution time of submitted jobs, for instance when SLAs are established with end-users. In this work, we propose and validate a hybrid approach exploiting both queuing networks and support vector regression, in order to achieve a good accuracy without too many costly experiments on a real setup. The experimental results show how the proposed approach attains a 21% improvement in accuracy over applying machine learning techniques without any support from analytical models